Artificial Intelligence & Robotics

My Latest Study on AI Governance

by on April 20, 2023 · 0 comments

The R Street Institute has just released my latest study on AI governance and how to address “alignment” concerns in a bottom-up fashion. The 40-page report is entitled, “Flexible, Pro-Innovation Governance Strategies for Artificial Intelligence.”

My report asks, is it possible to address AI alignment without starting with the Precautionary Principle as the governance baseline default? I explain how that is indeed possible. While some critics claim that no one is seriously trying to deal with AI alignment today, my report explains how no technology in history has been more heavily scrutinized this early in its life-cycle as AI, machine learning and robotics. The number of ethical frameworks out there already is astonishing. We don’t have too few alignment frameworks; we probably have too many!

We need to get serious about bringing some consistency to these efforts and figure out more concrete ways to a culture of safety by embedding ethics-by-design. But there is an equally compelling interest in ensuring that algorithmic innovations are developed and made widely available to society.

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On “Pausing” AI

by on April 7, 2023 · 0 comments

Recently, the Future of Life Institute released an open letter that included some computer science luminaries and others calling for a 6-month “pause” on the deployment and research of “giant” artificial intelligence (AI) technologies. Eliezer Yudkowsky, a prominent AI ethicist, then made news by arguing that the “pause” letter did not go far enough and he proposed that governments consider “airstrikes” against data processing centers, or even be open to the use of nuclear weapons. This is, of course, quite insane. Yet, this is the state of the things today as a AI technopanic seems to growing faster than any of the technopanic that I’ve covered in my 31 years in the field of tech policy—and I have covered a lot of them.

In a new joint essay co-authored with Brent Orrell of the American Enterprise Institute and Chris Messerole of Brookings, we argue that “the ‘pause’ we are most in need of is one on dystopian AI thinking.” The three of us recently served on a blue-ribbon Commission on Artificial Intelligence Competitiveness, Inclusion, and Innovation, an independent effort assembled by the U.S. Chamber of Commerce. In our essay, we note how:

Many of these breakthroughs and applications will already take years to work their way through the traditional lifecycle of development, deployment, and adoption and can likely be managed through legal and regulatory systems that are already in place. Civil rights laws, consumer protection regulations, agency recall authority for defective products, and targeted sectoral regulations already govern algorithmic systems, creating enforcement avenues through our courts and by common law standards allowing for development of new regulatory tools that can be developed as actual, rather than anticipated, problems arise.

“Instead of freezing AI we should leverage the legal, regulatory, and informal tools at hand to manage existing and emerging risks while fashioning new tools to respond to new vulnerabilities,” we conclude. Also on the pause idea, it’s worth checking out this excellent essay from Bloomberg Opinion editors on why “An AI ‘Pause’ Would Be a Disaster for Innovation.”

The problem is not with the “pause” per se. Even if the signatories could somehow enforce a worldwide stop-work order, six months probably wouldn’t do much to halt advances in AI. If a brief and partial moratorium draws attention to the need to think seriously about AI safety, it’s hard to see much harm. Unfortunately, a pause seems likely to evolve into a more generalized opposition to progress.

The editors continue on to rightly note:

This is a formula for outright stagnation. No one can ever be fully confident that a given technology or application will only have positive effects. The history of innovation is one of trial and error, risk and reward. One reason why the US leads the world in digital technology — why it’s home to virtually all the biggest tech platforms — is that it did not preemptively constrain the industry with well-meaning but dubious regulation. It’s no accident that all the leading AI efforts are American too.

That is 100% right, and I appreciate the Bloomberg editors linking to my latest study on AI governance when they made this point. In this new R Street Institute study, I explain why “Getting AI Innovation Culture Right,” is essential to make sure we can enjoy the many benefits that algorithmic systems offer, while also staying competitive in the global race for competitive advantage in this space. Continue reading →

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What Policy Vision for Artificial Intelligence?

by on April 2, 2023 · 0 comments

In my latest R Street Institute report, I discuss the importance of “Getting AI Innovation Culture Right.” This is the first of a trilogy of major reports on what sort of policy vision and set of governance principles should guide the development of  artificial intelligence (AI), algorithmic systems, machine learning (ML), robotics, and computational science and engineering more generally. More specifically, these reports seek to answer the question, Can we achieve AI safety without innovation-crushing top-down mandates and massive new regulatory bureaucracies? 

These questions are particular pertinent as we just made it through a week in which we’ve seen a major open letter issued that calls for a 6-month freeze on the deployment of AI technologies, while a prominent AI ethicist argued that governments should go further and consider airstrikes data processing centers even if the exchange of nuclear weapons needed to be considered! On top of that, Italy became the first major nation to ban ChatGPT, the popular AI-enabled chatbot created by U.S.-based OpenAI.

My report begins from a different presumption: AI, ML and algorithmic technologies present society with enormously benefits and, while real risks are there, we can find better ways of addressing them. Continue reading →

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Why Isn’t Everyone Already Unemployed Due to Automation?

by on March 11, 2023 · 0 comments

I have a new R Street Institute policy study out this week doing a deep dive into the question: “Can We Predict the Jobs and Skills Needed for the AI Era?” There’s lots of hand-wringing going on today about AI and the future of employment, but that’s really nothing new. In fact, in light of past automation panics, we might want to step back and ask: Why isn’t everyone already unemployed due to technological innovation?

To get my answers, please read the paper! In the meantime, here’s the executive summary:

To better plan for the economy of the future, many academics and policymakers regularly attempt to forecast the jobs and worker skills that will be needed going forward. Driving these efforts are fears about how technological automation might disrupt workers, skills, professions, firms and entire industrial sectors. The continued growth of artificial intelligence (AI), robotics and other computational technologies exacerbate these anxieties.

Yet the limits of both our collective knowledge and our individual imaginations constrain well-intentioned efforts to plan for the workforce of the future. Past attempts to assist workers or industries have often failed for various reasons. However, dystopian predictions about mass technological unemployment persist, as do retraining or reskilling programs that typically fail to produce much of value for workers or society. As public efforts to assist or train workers move from general to more specific, the potential for policy missteps grows greater. While transitional-support mechanisms can help alleviate some of the pain associated with fast-moving technological disruption, the most important thing policymakers can do is clear away barriers to economic dynamism and new opportunities for workers.

I do discuss some things that government can do to address automation fears at the end of the paper, but it’s important that policymakers first understand all the mistakes we’ve made with past retraining and reskilling efforts. The easiest thing to do to help in the short-term is clear away barriers to labor mobility and economic dynamism, I argue. Again, read the study for details.

For more info on other AI policy developments, check out my running list of research on AI, ML robotics policy.

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US Chamber AI Commission Launches

by on March 11, 2023 · 0 comments

This week, the U.S. Chamber of Commerce Commission on Artificial Intelligence Competitiveness, Inclusion, and Innovation (AI Commission) released a major report on the policy considerations surrounding AI, machine learning (ML) and algorithmic systems. The 120-page report concluded that “AI technology offers great hope for increasing economic opportunity, boosting incomes, speeding life science research at reduced costs, and simplifying the lives of consumers.” It was my honor to serve as one of the commissioners on the AI Commission and contribute to the report.

Over at the R Street Institute blog, I offer a quick summary of the major findings and recommendations from the report and argue that, along with the National Institute of Standards and Technology (NIST)’s recently released AI Risk Management Framework, the AI Commission report offers, “a constructive, consensus-driven framework for algorithmic governance rooted in flexibility, collaboration and iterative policymaking. This represents the uniquely American approach to AI policy that avoids the more heavy-handed regulatory approaches seen in other countries and it can help the United States again be a global leader in an important new technological field,” I conclude. Check out the blog post and the full AI Commission report if you are following debates of algorithmic policy issues. There’s lot of important material in there.

For more info on AI policy developments, check out my running list of research on AI, ML robotics policy.

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7 AI Policy Issues to Watch in 2023 and Beyond

by on February 10, 2023 · 0 comments

In my latest R Street Institute blog post, “Mapping the AI Policy Landscape Circa 2023: Seven Major Fault Lines,” I discuss the big issues confronting artificial intelligence and machine learning in the coming year and beyond. I note that the AI regulatory proposals are multiplying fast and coming in two general varieties: broad-based and targeted. Broad-based algorithmic regulation would address the use of these technologies in a holistic fashion across many sectors and concerns. By contrast, targeted algorithmic regulation looks to address specific AI applications or concerns. In the short-term, it is more likely that targeted or “sectoral” regulatory proposals have a chance of being implemented.

I go on to identify seven major issues of concern that will drive these policy proposals. They include:

1) Privacy and Data Collection

2) Bias and Discrimination

3) Free Speech and Disinformation

4) Kids’ Safety

5) Physical Safety and Cybersecurity

6) Industrial Policy and Workforce Issues

7) National Security and Law Enforcement Issues

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AI Policy Research: My Year in Review

by on December 26, 2022 · 0 comments

I spent much of 2022 writing about the growing policy debate over artificial intelligence, machine learning, robotics, and the Computational Revolution more generally. Here are some of the major highlights of my work on this front.

All these essays + dozens more can be found on my: “Running List of My Research on AI, ML & Robotics Policy.” I have several lengthy studies and many shorter essays coming in the first half of 2023.

Finally, here is a Federalist Society podcast discussion about AI policy hosted by Jennifer Huddleston in which Hodan Omaar of ITIF and I offer a big picture overview of where things are headed next.

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Tech Regulation Will Increasingly Be Driven Through the Prism of “Algorithmic Fairness”

by on November 6, 2022 · 0 comments

We are entering a new era for technology policy in which many pundits and policymakers will use “algorithmic fairness” as a universal Get Out of Jail Free card when they push for new regulations on digital speech and innovation. Proposals to regulate things like “online safety,” “hate speech,” “disinformation,” and “bias” among other things often raise thorny definitional questions because of their highly subjective nature. In the United States, efforts by government to control these things will often trigger judicial scrutiny, too, because restraints on speech violate the First Amendment. Proponents of prior restraint or even ex post punishments understand this reality and want to get around it. Thus, in an effort to avoid constitutional scrutiny and lengthy court battles, they are engaged in a rebranding effort and seeking to push their regulatory agendas through a techno-panicky prism of “algorithmic fairness” or “algorithmic justice.”

Hey, who could possibly be against FAIRNESS and JUSTICE? Of course, the devil is always in the details as Neil Chilson and I discuss in our new paper for the The Federalist Society and Regulatory Transparency Project on, “The Coming Onslaught of ‘Algorithmic Fairness’ Regulations.” We document how federal and state policymakers from both parties are currently considering a variety of new mandates for artificial intelligence (AI), machine learning, and automated systems that, if imposed, “would thunder through our economy with one of the most significant expansions of economic and social regulation – and the power of the administrative state – in recent history.” Continue reading →

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We Need to Get All the Smart People in a Room & Have a Conversation

by on October 16, 2022 · 0 comments

For my latest Discourse column (“We Really Need To ‘Have a Conversation’ About AI … or Do We?”), I discuss two commonly heard lines in tech policy circles.

1) “We need to have conversation about the future of AI and the risks that it poses.”

2) “We should get a bunch of smart people in a room and figure this out.”

I note that, if you’ve read enough essays, books or social media posts about artificial intelligence (AI) and robotics—among other emerging technologies—then chances are you’ve stumbled on variants of these two arguments many times over. They are almost impossible to disagree with in theory, but when you start to investigate what they actually mean in practice, they are revealed to be largely meaningless rhetorical flourishes which threaten to hold up meaningful progress on the AI front. I continue on to argue in my essay that:

I’m not at all opposed to people having serious discussions about the potential risks associated with AI, algorithms, robotics or smart machines. But I do have an issue with: (a) the astonishing degree of ambiguity at work in the world of AI punditry regarding the nature of these “conversations,” and, (b) the fact that people who are making such statements apparently have not spent much time investigating the remarkable number of very serious conversations that have already taken place, or which are ongoing, about AI issues.

In fact, it very well could be the case that we have too many conversations going on currently about AI issues and that the bigger problem is instead one of better coordinating the important lessons and best practices that we have already learned from those conversations.

I then unpack each of those lines and explain what is wrong with them in more detail. Continue reading →

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No Goldilocks Formula for Content Moderation in Social Media or the Metaverse, But Algorithms Still Help

by on September 13, 2022 · 0 comments

[Cross-posted from Medium.]

In an age of hyper-partisanship, one issue unites the warring tribes of American politics like no other: hatred of “Big Tech.” You know, those evil bastards who gave us instantaneous access to a universe of information at little to no cost. Those treacherous villains! People are quick to forget the benefits of moving from a world of Information Poverty to one of Information Abundance, preferring to take for granted all they’ve been given and then find new things to complain about.

But what mostly unites people against large technology platforms is the feeling that they are just too big or too influential relative to other institutions, including government. I get some of that concern, even if I strongly disagree with many of their proposed solutions, such as the highly dangerous sledgehammer of antitrust breakups or sweeping speech controls. Breaking up large tech companies would not only compromise the many benefits they provide us with, but it would undermine America’s global standing as a leader in information and computational technology. We don’t want that. And speech codes or meddlesome algorithmic regulations are on a collision course with the First Amendment and will just result in endless litigation in the courts.

There’s a better path forward. As President Ronald Reagan rightly said in 1987 when vetoing a bill to reestablish the Fairness Doctrine, “History has shown that the dangers of an overly timid or biased press cannot be averted through bureaucratic regulation, but only through the freedom and competition that the First Amendment sought to guarantee.” In other words, as I wrote in a previous essay about “The Classical Liberal Approach to Digital Media Free Speech Issues,” more innovation and competition are always superior to more regulation when it comes to encouraging speech and speech opportunities.

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